TfidfVectorizer

Convert a collection of raw documents to a matrix of TF-IDF features.

Equivalent to CountVectorizer followed by TfidfTransformer.

Constructors

constructor()

Signature

new TfidfVectorizer(opts?: object): TfidfVectorizer;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.analyzer?`	`"word"` \| `"char"` \| `"char_wb"`	Whether the feature should be made of word or character n-grams. Option ‘char_wb’ creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. `Default Value` `'word'`
`opts.binary?`	`boolean`	If `true`, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. (Set idf and normalization to `false` to get 0/1 outputs). `Default Value` `false`
`opts.decode_error?`	`"ignore"` \| `"strict"` \| `"replace"`	Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other values are ‘ignore’ and ‘replace’. `Default Value` `'strict'`
`opts.dtype?`	`any`	Type of the matrix returned by fit_transform() or transform().
`opts.encoding?`	`string`	If bytes or files are given to analyze, this encoding is used to decode. `Default Value` `'utf-8'`
`opts.input?`	`"filename"` \| `"file"` \| `"content"`	If `'filename'`, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. `Default Value` `'content'`
`opts.lowercase?`	`boolean`	Convert all characters to lowercase before tokenizing. `Default Value` `true`
`opts.max_df?`	`number`	When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not `undefined`. `Default Value` `1`
`opts.max_features?`	`number`	If not `undefined`, build a vocabulary that only consider the top `max\_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not `undefined`.
`opts.min_df?`	`number`	When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not `undefined`. `Default Value` `1`
`opts.ngram_range?`	`any`	The lower and upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. For example an `ngram\_range` of `(1, 1)` means only unigrams, `(1, 2)` means unigrams and bigrams, and `(2, 2)` means only bigrams. Only applies if `analyzer` is not callable.
`opts.norm?`	`"l1"` \| `"l2"`	Each output row will have unit norm, either: `Default Value` `'l2'`
`opts.preprocessor?`	`any`	Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if `analyzer` is not callable.
`opts.smooth_idf?`	`boolean`	Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. `Default Value` `true`
`opts.stop_words?`	`any`[] \| `"english"`	If a string, it is passed to _check_stop_list and the appropriate stop list is returned. ‘english’ is currently the only supported string value. There are several known issues with ‘english’ and you should consider an alternative (see Using stop words). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if `analyzer \== 'word'`. If `undefined`, no stop words will be used. In this case, setting `max\_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms.
`opts.strip_accents?`	`"ascii"` \| `"unicode"`	Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have a direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. `undefined` (default) does nothing. Both ‘ascii’ and ‘unicode’ use NFKD normalization from `unicodedata.normalize` (opens in a new tab).
`opts.sublinear_tf?`	`boolean`	Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). `Default Value` `false`
`opts.token_pattern?`	`string`	Regular expression denoting what constitutes a “token”, only used if `analyzer \== 'word'`. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted.
`opts.tokenizer?`	`any`	Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if `analyzer \== 'word'`.
`opts.use_idf?`	`boolean`	Enable inverse-document-frequency reweighting. If `false`, idf(t) = 1. `Default Value` `true`
`opts.vocabulary?`	`any`	Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents.

Returns

TfidfVectorizer

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:25 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:23 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:22 (opens in a new tab)

_py

PythonBridge

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:21 (opens in a new tab)

id

string

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:18 (opens in a new tab)

opts

any

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:19 (opens in a new tab)

Accessors

fixed_vocabulary_

True if a fixed vocabulary of term to indices mapping is provided by the user.

Signature

fixed_vocabulary_(): Promise<boolean>;

Returns

Promise<boolean>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:621 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:171 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:175 (opens in a new tab)

stop_words_

Terms that were ignored because they either:

Signature

stop_words_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:646 (opens in a new tab)

vocabulary_

A mapping of terms to feature indices.

Signature

vocabulary_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:596 (opens in a new tab)

Methods

build_analyzer()

Return a callable to process input data.

The callable handles preprocessing, tokenization, and n-grams generation.

Signature

build_analyzer(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:265 (opens in a new tab)

build_preprocessor()

Return a function to preprocess the text before tokenization.

Signature

build_preprocessor(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:293 (opens in a new tab)

build_tokenizer()

Return a function that splits a string into a sequence of tokens.

Signature

build_tokenizer(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:321 (opens in a new tab)

decode()

Decode the input into a string of unicode symbols.

The decoding strategy depends on the vectorizer parameters.

Signature

decode(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.doc?`	`string`	The string to decode.

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:351 (opens in a new tab)

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:246 (opens in a new tab)

fit()

Learn vocabulary and idf from training set.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.raw_documents?`	`any`	An iterable which generates either str, unicode or file objects.
`opts.y?`	`any`	This parameter is not needed to compute tfidf.

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:384 (opens in a new tab)

fit_transform()

Learn vocabulary and idf, return document-term matrix.

This is equivalent to fit followed by transform, but more efficiently implemented.

Signature

fit_transform(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.raw_documents?`	`any`	An iterable which generates either str, unicode or file objects.
`opts.y?`	`any`	This parameter is ignored.

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:424 (opens in a new tab)

get_feature_names_out()

Get output feature names for transformation.

Signature

get_feature_names_out(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.input_features?`	`any`	Not used, present here for API consistency by convention.

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:462 (opens in a new tab)

get_stop_words()

Build or fetch the effective stop words list.

Signature

get_stop_words(opts: object): Promise<any>;

Parameters

Name	Type
`opts`	`object`

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:498 (opens in a new tab)

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:184 (opens in a new tab)

inverse_transform()

Return terms per document with nonzero entries in X.

Signature

inverse_transform(opts: object): Promise<any[]>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`	Document-term matrix.

Returns

Promise<any[]>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:526 (opens in a new tab)

transform()

Transform documents to document-term matrix.

Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform).

Signature

transform(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.raw_documents?`	`any`	An iterable which generates either str, unicode or file objects.

Returns

Promise<any>

Defined in: generated/feature_extraction/text/TfidfVectorizer.ts:563 (opens in a new tab)

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